Browsing by Author "Vera, Enrique Gabriel"

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Geographic-information-based stochastic optimization model for multi-microgrid planning
(Elsevier, 2023-04-01) Vera, Enrique Gabriel; Cañizares, Claudio; Pirnia, Mehrdad
This paper presents a model for the realistic planning of multi-microgrids in the context of Active Distribution Networks with the assistance of Geographic Information Systems. The model considers the distribution system grid as well as the geographic features of the Region of Interest. It also includes long-term purchase decisions and short-term operational constraints, and considers uncertainties associated with electricity demand and Renewable Energy Resources using an existing Two-Stage Stochastic Programming approach. Geographic Information Systems along with Deep Learning are used to estimate the areas of rooftops within the Region of Interest and model the Low Voltage grid. The planning model is used to study the feasibility of implementing a multi-microgrid system consisting of 4 individual microgrids at an Active Distribution Network in a municipality in the state of São Paulo, Brazil. The results of the model presented in this paper are compared with the results obtained using Monte Carlo Simulations and an existing, less detailed, Two Stage Stochastic model. It is demonstrated that the stochastic solutions are close to those obtained with Monte Carlo at a lower computational cost, and that the use of Geographic Information allows to determine both the capacity and location of the PV panels, batteries, and distribution transformers on the microgrids grid, thus providing more precise and useful planning results.
Renewable Energy Integration in Canadian Remote Community Microgrids: The Feasibility of Hydrogen and Gas Generation
(Institute of Electrical and Electronics Engineers (IEEE), 2020-12-02) Vera, Enrique Gabriel; Canizares, Claudio; Pirnia, Mehrdad
Approximately 1.1 Billion, or 14%, of the global population do not have access to electricity due to the challenges associated with energy supply. Around 84% of those without electricity access reside in rural areas, with more than 95% being in sub-Saharan Africa and the developing parts of Asia. In Canada, about 72% of off-grid aboriginal and nonaboriginal communities use fossil fuel (oil: 71%, natural gas: 0.8%) as their main source of electricity generation, and only 4.7% of these communities rely on renewable energy sources (RES). In addition, 17.9% fulfill their energy demand through interconnections with other communities as they don?t have enough resources to support their own needs. The remaining 5.6% are reported to rely on unknown sources of electricity (see Arriaga et.al. 2014).
Two-Stage Stochastic Optimization Model for Multi-Microgrid Planning
(Institute of Electrical and Electronics Engineers (IEEE), 2022-10-03) Vera, Enrique Gabriel; Cañizares, Claudio A.; Pirnia, Mehrdad; Guedes, Tatiana Pontual; Trujillo, Joel David Melo
This paper presents a Two Stage stochastic Programming (TSSP) model for the planning of Multi-Microgrids (MMGs) in Active Distribution Networks (ADNs). The model aims to minimize the total costs while benefiting from interconnections of Microgrids (MGs), considering uncertainties associated with electricity demand and Renewable Energy Sources (RESs). The associated uncertainties are analyzed using Geometric Brownian Motion (GBM) and probability distribution functions (pdfs). The model includes long-term purchase decisions and short-term operational constraints, using Geographical information Systems (GIS) to realistically estimate rooftop solar limits. The planning model is used to study the feasibility of implementing an MMG system consisting of 4 individual Microgrids (MGs) at an ADN in a municipality in the state of São Paulo, Brazil. The results show that the TSSP model tends to be less conservative than the deterministic planning model, which is based on simple and pessimistic reserve constraints, while performing faster than a simple Stochastic Linear Programming (SLP) algorithm, with higher accuracy.